[1]} provides a general sense of representational power of a broad range of SSL models based on the downstream task performance. Several studies have probed representations themselves learned by speech SSL [2]}, [3]}, [4]}, [5]}, [6]}. [4]} and [5]} reported layer-wise phoneme encoding for their developed unsupervised models.
[2]} characterizes across layer patterns of acoustic, phonetic, and semantic encoding. [3]} conducted non-linear probing analyses using more extensive and fine-grained labels of speech features. Articulations are also included in the scope of [3]}, [6]}, but those are hand labeled pronunciations which are discrete and lack context dependent information. Up to our knowledge, none of the prior works directly deals with continuous articulations represented as EMA.
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